AI Operations

Computational resources refer to the hardware and software needed for processing data and running algorithms in AI.

Continuous Integration ML involves regularly integrating machine learning code changes to enhance collaboration and streamline deployment.

Data Orchestration involves coordinating data workflows across various systems to ensure timely and accurate data processing.

DevOps ML integrates machine learning practices with DevOps methodologies for streamlined AI development and deployment.

Machine Learning Operations (MLOps) integrates ML model development and deployment for efficient and reliable AI systems.

MLOps is the practice of integrating machine learning into DevOps to streamline the deployment and management of ML models.

Model Implementation refers to the process of deploying an AI model into a production environment for real-world use.

Model Monitoring involves tracking AI models' performance and behavior post-deployment to ensure reliability and accuracy.

Model rollback is the process of reverting an AI model to a previous version after performance degradation.

Optimized Operation refers to the processes and techniques used to enhance the efficiency of AI systems.

The Overall Pipeline in AI refers to the complete process from data collection to model deployment and evaluation.